Gymnasium

#gym   #gymnasium   #ai   #reinforcement learning  

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Table of Content

Gymnasium

Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments more

install

pip install gymnasium
# environment
# quat are mandatory
pip install "gymnasium[box2d]"

Gym Environment

install

pip install "gymnasium[classic-control]"
pip install "gymnasium[atari]"
...

Course

• Python Reinforcement Learning using Gymnasium – Full Course
• Reinforcement learning with Gymnasium (Farama Foundation)
• Gymnasium

Gym main concepts

• Action Space: Is the set of possible actions that an agent can take in the environment.
• Observation Space: Is the set of possible states that agent can observe in the environment.
• Episode: Is a complete run from initial state until terminate state. each episode is composed of a sequence of states, actions and rewards.

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Gym CartPole

cart pole

basic

import gymnasium as gym

env = gym.make('CartPole-v1', render_mode='human')
env.reset()

print(f"observation_space max: {env.observation_space.high}")
print(f"observation_space min: {env.observation_space.low}")
print(f"Action space: {env.action_space.n}")



done = False
while not done:
    action = env.action_space.sample()
    new_state, reward, done, _, _ = env.step(action)
    env.render()

output

observation_space max: [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]
observation_space min: [-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]
Action space: 2

• action space

• observation space